DESCRIPTION (provided by investigator): The field of schizophrenia genetics has reached a point where several strong susceptibility genes have been proposed, but understanding of the functional significance of the implicated alleles or haplotypes and how they interact with each other, is still lacking. Using gene targeting and transgenesis approaches, as well as available mouse lines, we have established a cohort of 6 mouse models of strong candidate schizophrenia susceptibility genes, which we propose to use to elucidate the structure of the genetic networks that modulate the genetic risk of the disease. Analysis of the expression profile in the hippocampus and prefrontal cortex of these 6 mutant lines and the 5 double-mutant lines we propose to generate, along with cross-species comparison of gene expression patterns, will allow us to obtain an unbiased evaluation of the transcriptional programs affected by impaired function or expression of these genes, reflecting downstream effects of the mutation, or adaptive and compensatory changes. It will also allow us to assess the relative similarities or differences among gene networks affected in different mouse models and therefore facilitate the identification of key signaling pathways that modulate the disease risk. By deconstructing the genetic component of schizophrenia using reliable animal models under conditions that are not confounded by the effects of the treatment or the disease itself, our approach offers unique advantages over traditional expression profiling in diseased brains. Another strong point of this proposal is the fact that all these mouse models are available within our laboratory and will be examined under identical controlled conditions using the same methodologies. Given the heterogeneity of the disease and variability in experimental conditions across sites, this is a unique opportunity to study accurately the effect of each of these genes and combinations of them. The importance and timely nature of this proposal cannot be overemphasized. Our ability to analyze transcriptional profiles of entire genomes for any one mutation is likely to transform the traditional view of a simple disease gene or genetic pathway, into a more complex concept of genetic networks and at the same time provide a wealth of useful drug targets.